To see the code, click on the CODE button. You can also download the whole R Markdown file from the drop down menu on the top right corner.

library(tidyverse)
library(ggtext)
library(patchwork)
library(readxl)
library(nullabor)
library(here)
library(janitor)
library(scales)
#theme_set(theme_classic())
knitr::opts_chunk$set(fig.path = "images/",
                      dev = c("png", "pdf", "svg"))
df_full <- read_xlsx(here("data/MaskedCoverage-Fig3.xlsx")) %>% 
  clean_names() %>% 
  add_row(state = c("OR", "WY", "SD", "WV", "DC", "AL")) %>% 
  mutate(row = case_when(
    state %in% c("ME") ~ 1L,
    state %in% c("VT", "NH") ~ 2L,
    state %in% c("WA", "ID", "MT", "ND", "MN", "IL", "WI", "MI", "NY", "RI", "MA") ~ 3L,
    state %in% c("OR", "NV", "WY", "SD", "IA", "IN", "OH", "PA", "NJ", "CT") ~ 4L,
    state %in% c("CA", "UT", "CO", "NE", "MO", "KY", "WV", "VA", "MD", "DE") ~ 5L,
    state %in% c("AZ", "NM", "KS", "AR", "TN", "NC", "SC", "DC") ~ 6L,
    state %in% c("OK", "LA", "MS", "AL", "GA") ~ 7L,
    state %in% c("TX", "FL") ~ 8L,
                         TRUE ~ 0L),
    col = case_when(
      state %in% c("WA", "OR", "CA") ~ 1L,
      state %in% c("ID", "NV", "UT", "AZ") ~ 2L,
      state %in% c("MT", "WY", "CO", "NM") ~ 3L,
      state %in% c("ND", "SD", "NE", "KS", "OK", "TX") ~ 4L,
      state %in% c("MN", "IA", "MO", "AR", "LA") ~ 5L,
      state %in% c("IL", "IN", "KY", "TN", "MS") ~ 6L,
      state %in% c("WI", "OH", "WV", "NC", "AL") ~ 7L,
      state %in% c("MI", "PA", "VA", "SC", "GA") ~ 8L,
      state %in% c("NY", "NJ", "MD", "DC", "FL") ~ 9L,
      state %in% c("VT", "RI", "CT", "DE") ~ 10L,
      state %in% c("ME", "NH", "MA") ~ 11L,
      TRUE ~ 0L
    ))

df_miss <- df_full %>% 
  filter(!is.na(readmission_rate))
g1 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = readmission_rate * 100), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(readmission_rate, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$readmission_rate * 100), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse() +
  theme(plot.margin = margin(r = 30)) +
  labs(color = "Readmission",
       size = "Coverage")

g2 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(colorectal_cancer_screenings/100, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse()  +
  labs(color = "Cancer Screening",
       size = "Coverage")

g1 + g2 + plot_layout(guides = "collect") 
This figure recreates Figure 3 in Basole et al. (2021).

Figure S1: This figure recreates Figure 3 in Basole et al. (2021).

theme_set(theme_classic())
g1 <- ggplot(df_miss, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Cancer Screening (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 73, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$colorectal_cancer_screenings), 0.001)}")) 

g2 <- ggplot(df_miss, aes(coverage_obscured * 100, readmission_rate * 100)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Readmission (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 15.3, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$readmission_rate), 0.001)}")) 

g1 + g2 
This is an alternative graph design for Figure 1.

Figure S2: This is an alternative graph design for Figure 1.

set.seed(2021)
lineup_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_miss, n = 20, pos = 3)
plot_lineup_theirs <- ggplot(lineup_data, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  theme_void() + 
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(1, 5)) +
  scale_y_reverse(expand = c(0.1, 0.2))  +
  guides(color = "none", size = "none") + 
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5))

plot_lineup_theirs
The lineup for the tile grid plot.

Figure S3: The lineup for the tile grid plot.

plot_lineup_ours <- ggplot(lineup_data, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +  
  geom_smooth(method = loess, formula = y ~ x) +
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5),
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.line = element_blank(),
        axis.ticks.length = unit(0, "pt"))

plot_lineup_ours
The lineup for the scatter plot.

Figure S4: The lineup for the scatter plot.

Same plots with higher associations between variables

The following are plots based on data that purposely modifies cancer screening to induce a higher association with the coverage.

df_false <- df_miss %>% 
  arrange(coverage_obscured) %>% 
  mutate(colorectal_cancer_screenings = sort(colorectal_cancer_screenings))

lineup_false_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_false, n = 20, pos = 5)
plot_lineup_theirs %+% lineup_false_data
Which plot looks the most strikingly different to you?

Figure S5: Which plot looks the most strikingly different to you?

plot_lineup_ours %+% lineup_false_data
The above shows a lineup for data that was purposely manipulated so that two variables have a higher association. How easy was it to spot the data plot compared to Figure 5?

Figure S6: The above shows a lineup for data that was purposely manipulated so that two variables have a higher association. How easy was it to spot the data plot compared to Figure 5?

Acknowledgement

We thank Basole et al. (2021) for supplying us the synthetic data to draw the above plots.

Reference

Session Information
sessioninfo::session_info()
## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.0.1 (2020-06-06)
##  os       macOS  10.16                
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_AU.UTF-8                 
##  ctype    en_AU.UTF-8                 
##  tz       Australia/Melbourne         
##  date     2021-09-19                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package     * version date       lib source                           
##  assertthat    0.2.1   2019-03-21 [2] CRAN (R 4.0.0)                   
##  backports     1.2.1   2020-12-09 [1] CRAN (R 4.0.2)                   
##  bookdown      0.22.17 2021-08-07 [1] Github (rstudio/bookdown@9615b14)
##  broom         0.7.9   2021-07-27 [1] CRAN (R 4.0.2)                   
##  bslib         0.2.5   2021-05-12 [1] CRAN (R 4.0.1)                   
##  cellranger    1.1.0   2016-07-27 [2] CRAN (R 4.0.0)                   
##  class         7.3-19  2021-05-03 [2] CRAN (R 4.0.2)                   
##  cli           3.0.1   2021-07-17 [1] CRAN (R 4.0.2)                   
##  cluster       2.1.2   2021-04-17 [2] CRAN (R 4.0.2)                   
##  colorspace    2.0-1   2021-05-04 [1] CRAN (R 4.0.2)                   
##  crayon        1.4.1   2021-02-08 [1] CRAN (R 4.0.2)                   
##  DBI           1.1.1   2021-01-15 [1] CRAN (R 4.0.2)                   
##  dbplyr        2.1.1   2021-04-06 [1] CRAN (R 4.0.2)                   
##  DEoptimR      1.0-8   2016-11-19 [2] CRAN (R 4.0.0)                   
##  digest        0.6.27  2020-10-24 [1] CRAN (R 4.0.2)                   
##  diptest       0.76-0  2021-05-04 [2] CRAN (R 4.0.2)                   
##  dplyr       * 1.0.7   2021-06-18 [1] CRAN (R 4.0.2)                   
##  ellipsis      0.3.2   2021-04-29 [1] CRAN (R 4.0.2)                   
##  evaluate      0.14    2019-05-28 [2] CRAN (R 4.0.0)                   
##  fansi         0.5.0   2021-05-25 [1] CRAN (R 4.0.2)                   
##  farver        2.1.0   2021-02-28 [1] CRAN (R 4.0.2)                   
##  flexmix       2.3-17  2020-10-12 [1] CRAN (R 4.0.2)                   
##  forcats     * 0.5.1   2021-01-27 [1] CRAN (R 4.0.2)                   
##  fpc           2.2-9   2020-12-06 [2] CRAN (R 4.0.2)                   
##  fs            1.5.0   2020-07-31 [1] CRAN (R 4.0.2)                   
##  generics      0.1.0   2020-10-31 [2] CRAN (R 4.0.2)                   
##  ggplot2     * 3.3.3   2020-12-30 [1] CRAN (R 4.0.1)                   
##  ggtext      * 0.1.1   2020-12-17 [1] CRAN (R 4.0.2)                   
##  glue          1.4.2   2020-08-27 [1] CRAN (R 4.0.2)                   
##  gridtext      0.1.4   2020-12-10 [1] CRAN (R 4.0.2)                   
##  gtable        0.3.0   2019-03-25 [2] CRAN (R 4.0.0)                   
##  haven         2.4.1   2021-04-23 [2] CRAN (R 4.0.2)                   
##  here        * 1.0.1   2020-12-13 [2] CRAN (R 4.0.2)                   
##  highr         0.9     2021-04-16 [2] CRAN (R 4.0.2)                   
##  hms           1.1.0   2021-05-17 [1] CRAN (R 4.0.2)                   
##  htmltools     0.5.1.1 2021-01-22 [1] CRAN (R 4.0.2)                   
##  httr          1.4.2   2020-07-20 [1] CRAN (R 4.0.2)                   
##  janitor     * 2.1.0   2021-01-05 [2] CRAN (R 4.0.2)                   
##  jquerylib     0.1.4   2021-04-26 [1] CRAN (R 4.0.2)                   
##  jsonlite      1.7.2   2020-12-09 [1] CRAN (R 4.0.2)                   
##  kernlab       0.9-29  2019-11-12 [2] CRAN (R 4.0.0)                   
##  knitr         1.33    2021-04-24 [1] CRAN (R 4.0.2)                   
##  labeling      0.4.2   2020-10-20 [1] CRAN (R 4.0.2)                   
##  lattice       0.20-44 2021-05-02 [2] CRAN (R 4.0.2)                   
##  lifecycle     1.0.0   2021-02-15 [1] CRAN (R 4.0.2)                   
##  lubridate     1.7.10  2021-02-26 [1] CRAN (R 4.0.2)                   
##  magrittr      2.0.1   2020-11-17 [1] CRAN (R 4.0.2)                   
##  markdown      1.1     2019-08-07 [2] CRAN (R 4.0.0)                   
##  MASS          7.3-54  2021-05-03 [1] CRAN (R 4.0.2)                   
##  Matrix        1.3-3   2021-05-04 [2] CRAN (R 4.0.2)                   
##  mclust        5.4.7   2020-11-20 [2] CRAN (R 4.0.2)                   
##  mgcv          1.8-35  2021-04-18 [2] CRAN (R 4.0.2)                   
##  modelr        0.1.8   2020-05-19 [2] CRAN (R 4.0.0)                   
##  modeltools    0.2-23  2020-03-05 [2] CRAN (R 4.0.0)                   
##  moments       0.14    2015-01-05 [2] CRAN (R 4.0.0)                   
##  munsell       0.5.0   2018-06-12 [2] CRAN (R 4.0.0)                   
##  nlme          3.1-152 2021-02-04 [2] CRAN (R 4.0.2)                   
##  nnet          7.3-16  2021-05-03 [2] CRAN (R 4.0.2)                   
##  nullabor    * 0.3.9   2020-02-25 [1] CRAN (R 4.0.2)                   
##  patchwork   * 1.1.1   2020-12-17 [1] CRAN (R 4.0.2)                   
##  pillar        1.6.2   2021-07-29 [1] CRAN (R 4.0.2)                   
##  pkgconfig     2.0.3   2019-09-22 [2] CRAN (R 4.0.0)                   
##  prabclus      2.3-2   2020-01-08 [2] CRAN (R 4.0.0)                   
##  purrr       * 0.3.4   2020-04-17 [2] CRAN (R 4.0.0)                   
##  R6            2.5.1   2021-08-19 [1] CRAN (R 4.0.1)                   
##  Rcpp          1.0.7   2021-07-07 [1] CRAN (R 4.0.2)                   
##  readr       * 2.0.1   2021-08-10 [1] CRAN (R 4.0.2)                   
##  readxl      * 1.3.1   2019-03-13 [2] CRAN (R 4.0.0)                   
##  reprex        2.0.0   2021-04-02 [1] CRAN (R 4.0.2)                   
##  rlang         0.4.11  2021-04-30 [1] CRAN (R 4.0.2)                   
##  rmarkdown     2.10    2021-08-06 [1] CRAN (R 4.0.1)                   
##  robustbase    0.93-7  2021-01-04 [2] CRAN (R 4.0.2)                   
##  rprojroot     2.0.2   2020-11-15 [1] CRAN (R 4.0.2)                   
##  rstudioapi    0.13    2020-11-12 [1] CRAN (R 4.0.1)                   
##  rvest         1.0.1   2021-07-26 [1] CRAN (R 4.0.2)                   
##  sass          0.4.0   2021-05-12 [1] CRAN (R 4.0.2)                   
##  scales      * 1.1.1   2020-05-11 [2] CRAN (R 4.0.0)                   
##  sessioninfo   1.1.1   2018-11-05 [2] CRAN (R 4.0.0)                   
##  snakecase     0.11.0  2019-05-25 [2] CRAN (R 4.0.0)                   
##  stringi       1.7.3   2021-07-16 [1] CRAN (R 4.0.2)                   
##  stringr     * 1.4.0   2019-02-10 [2] CRAN (R 4.0.0)                   
##  tibble      * 3.1.3   2021-07-23 [1] CRAN (R 4.0.2)                   
##  tidyr       * 1.1.3   2021-03-03 [1] CRAN (R 4.0.2)                   
##  tidyselect    1.1.1   2021-04-30 [1] CRAN (R 4.0.2)                   
##  tidyverse   * 1.3.1   2021-04-15 [1] CRAN (R 4.0.2)                   
##  tzdb          0.1.2   2021-07-20 [1] CRAN (R 4.0.2)                   
##  utf8          1.2.2   2021-07-24 [1] CRAN (R 4.0.2)                   
##  vctrs         0.3.8   2021-04-29 [1] CRAN (R 4.0.2)                   
##  withr         2.4.2   2021-04-18 [1] CRAN (R 4.0.2)                   
##  xfun          0.24    2021-06-15 [1] CRAN (R 4.0.2)                   
##  xml2          1.3.2   2020-04-23 [2] CRAN (R 4.0.0)                   
##  yaml          2.2.1   2020-02-01 [1] CRAN (R 4.0.2)                   
## 
## [1] /Users/etan0038/Library/R/4.0/library
## [2] /Library/Frameworks/R.framework/Versions/4.0/Resources/library
Basole, Rahul C., Elliot Bendoly, Aravind Chandrasekaran, and Kevin Linderman. 2021. “Visualization in Operations Management Research.” INFORMS Journal on Data Science (to appear).
---
title: "Supplementary material for \"Uprooting sub-standard visualisation practices for decision-making in operational management\""
bibliography: references.bib 
output:
  bookdown::html_document2:
    code_folding: "hide"
    theme: "paper"
    code_download: true
    number_sections: false
---

To see the code, click on the CODE button. You can also download the whole R Markdown file from the drop down menu on the top right corner. 

```{r setup, message = FALSE, warning = FALSE}
library(tidyverse)
library(ggtext)
library(patchwork)
library(readxl)
library(nullabor)
library(here)
library(janitor)
library(scales)
#theme_set(theme_classic())
knitr::opts_chunk$set(fig.path = "images/",
                      dev = c("png", "pdf", "svg"))
```


```{r data}
df_full <- read_xlsx(here("data/MaskedCoverage-Fig3.xlsx")) %>% 
  clean_names() %>% 
  add_row(state = c("OR", "WY", "SD", "WV", "DC", "AL")) %>% 
  mutate(row = case_when(
    state %in% c("ME") ~ 1L,
    state %in% c("VT", "NH") ~ 2L,
    state %in% c("WA", "ID", "MT", "ND", "MN", "IL", "WI", "MI", "NY", "RI", "MA") ~ 3L,
    state %in% c("OR", "NV", "WY", "SD", "IA", "IN", "OH", "PA", "NJ", "CT") ~ 4L,
    state %in% c("CA", "UT", "CO", "NE", "MO", "KY", "WV", "VA", "MD", "DE") ~ 5L,
    state %in% c("AZ", "NM", "KS", "AR", "TN", "NC", "SC", "DC") ~ 6L,
    state %in% c("OK", "LA", "MS", "AL", "GA") ~ 7L,
    state %in% c("TX", "FL") ~ 8L,
                         TRUE ~ 0L),
    col = case_when(
      state %in% c("WA", "OR", "CA") ~ 1L,
      state %in% c("ID", "NV", "UT", "AZ") ~ 2L,
      state %in% c("MT", "WY", "CO", "NM") ~ 3L,
      state %in% c("ND", "SD", "NE", "KS", "OK", "TX") ~ 4L,
      state %in% c("MN", "IA", "MO", "AR", "LA") ~ 5L,
      state %in% c("IL", "IN", "KY", "TN", "MS") ~ 6L,
      state %in% c("WI", "OH", "WV", "NC", "AL") ~ 7L,
      state %in% c("MI", "PA", "VA", "SC", "GA") ~ 8L,
      state %in% c("NY", "NJ", "MD", "DC", "FL") ~ 9L,
      state %in% c("VT", "RI", "CT", "DE") ~ 10L,
      state %in% c("ME", "NH", "MA") ~ 11L,
      TRUE ~ 0L
    ))

df_miss <- df_full %>% 
  filter(!is.na(readmission_rate))
```

(ref:mimicary) This figure recreates Figure 3 in @basole2021.

```{r mimic-original, fig.height = 8, fig.width = 18, fig.cap = "(ref:mimicary)"}
g1 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = readmission_rate * 100), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(readmission_rate, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$readmission_rate * 100), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse() +
  theme(plot.margin = margin(r = 30)) +
  labs(color = "Readmission",
       size = "Coverage")

g2 <- ggplot(df_miss, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  geom_text(data = df_full, aes(label = state), color = "black", nudge_y = 0.05) +
  geom_text(aes(label = percent(colorectal_cancer_screenings/100, 0.01)), nudge_y = -0.1, size = 2.5) +
  theme_void() +
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(3, 30)) +
  scale_y_reverse()  +
  labs(color = "Cancer Screening",
       size = "Coverage")

g1 + g2 + plot_layout(guides = "collect") 
```

(ref:fig3-alt) This is an alternative graph design for Figure \@ref(fig:mimic-original).

```{r fig3-alt, fig.height = 4, fig.width = 8, fig.cap = "(ref:fig3-alt)"}
theme_set(theme_classic())
g1 <- ggplot(df_miss, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Cancer Screening (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 73, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$colorectal_cancer_screenings), 0.001)}")) 

g2 <- ggplot(df_miss, aes(coverage_obscured * 100, readmission_rate * 100)) +
  geom_point() +
  labs(x = "Coverage (%)", y = "Readmission (%)") +
  geom_smooth(method = loess, formula = y ~ x) +
  annotate("richtext", x = 80, y = 15.3, label.color = NA, fill = "transparent", label = glue::glue("R<sup>2</sup> = {scales::comma(cor(df_miss$coverage_obscured, df_miss$readmission_rate), 0.001)}")) 

g1 + g2 
```

```{r lineup-data}
set.seed(2021)
lineup_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_miss, n = 20, pos = 3)
```

(ref:lineup-theirs) The lineup for the tile grid plot.

```{r lineup-theirs, fig.height = 10, fig.width = 10, fig.cap = "(ref:lineup-theirs)"}
plot_lineup_theirs <- ggplot(lineup_data, aes(col, row)) +
  geom_point(aes(size = coverage_obscured, color = colorectal_cancer_screenings), alpha = 0.8) +
  theme_void() + 
  scale_color_gradient2(low = "#3F6E9A", high = "#AB4C30", midpoint = median(df_miss$colorectal_cancer_screenings), mid = "#E7D9C6") +
  scale_size(range = c(1, 5)) +
  scale_y_reverse(expand = c(0.1, 0.2))  +
  guides(color = "none", size = "none") + 
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5))

plot_lineup_theirs
```

(ref:lineup-ours) The lineup for the scatter plot.

```{r lineup-ours, fig.height = 10, fig.width = 10, fig.cap = "(ref:lineup-ours)"}
plot_lineup_ours <- ggplot(lineup_data, aes(coverage_obscured * 100, colorectal_cancer_screenings)) +
  geom_point() +  
  geom_smooth(method = loess, formula = y ~ x) +
  facet_wrap(~.sample, ncol = 5) +
  scale_x_continuous(expand = c(0.1, 0.1)) + 
  theme(legend.position = "bottom",
        strip.text = element_text(size = 18, margin = margin(t = 3, b = 3)),
        strip.background = element_rect(color = "black", size = 1.5),
        axis.text = element_blank(),
        axis.title = element_blank(),
        axis.line = element_blank(),
        axis.ticks.length = unit(0, "pt"))

plot_lineup_ours
```



# Same plots with higher associations between variables

The following are plots based on data that purposely modifies cancer screening to induce a higher association with the coverage. 

```{r data-false}
df_false <- df_miss %>% 
  arrange(coverage_obscured) %>% 
  mutate(colorectal_cancer_screenings = sort(colorectal_cancer_screenings))

lineup_false_data <- null_permute("colorectal_cancer_screenings") %>% 
  lineup(true = df_false, n = 20, pos = 5)
```

(ref:lineup-theirs-false) Which plot looks the most strikingly different to you?

```{r lineup-theirs-false, fig.height = 10, fig.width = 10, fig.cap = "(ref:lineup-theirs-false)"}
plot_lineup_theirs %+% lineup_false_data
```

(ref:lineup-ours-false) The above shows a lineup for data that was purposely manipulated so that two variables have a higher association. How easy was it to spot the data plot compared to Figure \@ref(fig:lineup-theirs-false)?

```{r lineup-ours-false, fig.height = 10, fig.width = 10, fig.cap = "(ref:lineup-ours-false)"}
plot_lineup_ours %+% lineup_false_data
```


# Acknowledgement 

We thank @basole2021 for supplying us the synthetic data to draw the above plots. 

# Reference

<details>
<summary>Session Information</summary>
```{r session-info}
sessioninfo::session_info()
```
</details>
